ML and DL Approaches for Intelligent Wireless Sensor Networks

Abstract

Wireless sensor network (WSN) designed with low-powered sensor which can sense, communicate, and process. However, sensors inhibited with diminutive processing capacity, battery, computational complexity, and memory. Hence, designed WSNs need to be reliable to make sure of the appliance functionality. Researchers proposed various techniques for proficient WSN deployment, but they do not suggest innovative tools or models to afford them. To deal with these problems, the research community propose intelligent WSNs. Due to their irregular nature, wireless systems are an attractive application in data science because they are influenced with both natural occurrences (electromagnetic propagation) and human-made artifacts (hardware and software fundamentals built by humans). Data-driven approaches or machine learning (ML) exploit real-life data records to expand the systems’ insight activities. It facilitates the study of small, uncomplicated, large, and extra composite schemes to evaluate whether the gathering depends on the projected design. Wireless networks can show signs of irregular contacts among algorithms from various protocol layers, connections flanked by multiple devices, and hardware-specific influences. These connections may guide to a disparity flanked by real-world execution and design-time execution. Data science schemes can help with sensing the actual activities and probably help to correct it. This chapter discusses various reasons for opting for the intelligent sensor, data processing and mining techniques, ML in sensor nodes, deep learning taxonomy for designing intelligent WSNs, link evolution, modulation, and signal recognition techniques for intelligent WSNs.